Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

本文介绍了3D越野地形环境的安全，高效和敏捷的地面车导航算法。越野导航受到3D地形拓扑顶部不同地形条件引起的不确定的车辆 - 透水相互作用。现有的作品仅限于采用过度简化的车辆模型。拟议的算法从驱动数据中学习了地形引起的不确定性，并将学习的不确定性分布编码到路径评估的遍历成本中。然后，设计导航路径以优化不确定性吸引的横穿性成本，从而导致安全而敏捷的车辆操纵。确保实时执行，该算法将在图形处理单元（GPU）上运行的并行计算体系结构中进一步实现。

已经使用基于物理学的模型对非全面车辆运动进行了广泛的研究。使用这些模型时，使用线性轮胎模型来解释车轮/接地相互作用时的通用方法，因此可能无法完全捕获各种环境下的非线性和复杂动力学。另一方面，神经网络模型已在该域中广泛使用，证明了功能强大的近似功能。但是，这些黑盒学习策略完全放弃了现有的知名物理知识。在本文中，我们无缝将深度学习与完全不同的物理模型相结合，以赋予神经网络具有可用的先验知识。所提出的模型比大边距的香草神经网络模型显示出更好的概括性能。我们还表明，我们的模型的潜在特征可以准确地表示侧向轮胎力，而无需进行任何其他训练。最后，我们使用从潜在特征得出的本体感受信息开发了一种风险感知的模型预测控制器。我们在未知摩擦下的两个自动驾驶任务中验证了我们的想法，表现优于基线控制框架。

神经网络已越来越多地用于模型预测控制器（MPC）来控制非线性动态系统。但是，MPC仍然提出一个问题，即可实现的更新率不足以应对模型不确定性和外部干扰。在本文中，我们提出了一种新颖的控制方案，该方案可以使用MPC的神经网络动力学设计最佳的跟踪控制器，从而使任何现有基于模型的Feedforward Controller的插件扩展程序都可以应用于插件。我们还描述了我们的方法如何处理包含历史信息的神经网络，该信息不遵循一般的动态形式。该方法通过其在外部干扰的经典控制基准中的性能进行评估。我们还扩展了控制框架，以应用于具有未知摩擦的积极自主驾驶任务。在所有实验中，我们的方法的表现都优于比较的方法。我们的控制器还显示出低控制的水平，表明我们的反馈控制器不会干扰MPC的最佳命令。

Wire actuation in tendon-driven continuum robots enables the transmission of force from a distance, but it is understood that tension control problems can arise when a pulley is used to actuate two cables in a push-pull mode. This paper analyzes the relationship between angle of rotation, pressure, as well as variables of a single continuum unit in a quasi-static equilibrium. The primary objective of the quasi-static analysis was to output pressure and the analysis, given the tensions applied. Static equilibrium condition was established, and the bisection method was carried out for the angle of rotation. The function for the bisection method considered pressure-induced forces, friction forces, and weight. {\theta} was 17.14{\deg}, and p was 405.6 Pa when Tl and Ts were given the values of 1 N and 2 N, respectively. The results seemed to be consistent with the preliminary design specification, calling for further simulations and experiments.

与常规的闭合设定识别相反，开放式识别（OSR）假设存在未知类别，在训练过程中未被视为模型。 OSR中的一种主要方法是度量学习，其中对模型进行了训练以分离已知类别数据的类间表示。 OSR中的许多作品报告说，即使模型仅通过已知类别的数据进行培训，模型也会意识到未知数，并学会将未知类表征与已知类别表示分开。本文通过观察雅各布的代表规范来分析这种新兴现象。从理论上讲，我们表明已知集中的阶层内距离最小化会减少已知类表征的雅各布式规范，同时最大化已知集合中的阶层间距离会增加未知类别的雅各布式规范。因此，封闭式度量学习通过迫使其雅各布规范值有所不同，从而将未知的未知数与已知分开。我们通过使用标准OSR数据集的大量证据来验证我们的理论框架。此外，在我们的理论框架下，我们解释了标准的深度学习技术如何有助于OSR并将框架作为指导原则来开发有效的OSR模型。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.